PUXplore Multidisciplinary Journal of Engineering
PUXplore: Multidisciplinary Journal of Engineering is a peer-reviewed, open-access academic journal dedicated to the advancement of engineering research across diverse disciplines. Published by Parul University, the journal serves as a dynamic platform for scholars, researchers, industry professionals, and academicians to disseminate innovative findings, practical insights, and theoretical advancements that address real-world engineering challenges. Scope and Aim: The journal welcomes original research articles, review papers, technical notes, and case studies from all major branches of engineering, including but not limited to: Artificial Intelligence and Data Science Computer Science and Engineering Electrical, Electronics, and Communication Engineering Mechanical and Automobile Engineering Civil and Structural Engineering Chemical and Environmental Engineering Mechatronics, Robotics, and Embedded Systems Biomedical and Biotechnology Engineering Internet of Things (IoT), Cybersecurity, and Smart Systems ParulXplore aims to foster interdisciplinary collaboration, bridge academia and industry, and contribute to national and global engineering knowledge ecosystems. Emphasis is given to research that integrates innovation, sustainability, digital transformation, and technological excellence. Frequency: Monthly Article Format: Double-blind peer review | APA/IEEE referencing | Plagiarism check mandatory Language of Publication: English Mode of Access: Online (Open Access) Key Features: Multidisciplinary Focus with Engineering Core Rigorous Peer Review Process Fast and Transparent Publication Workflow No Article Processing Charges (APCs) [Subject to editorial policy] Indexed in National and International Databases [In progress] Editorial Board: The journal is supported by a distinguished editorial board comprising leading academicians, researchers, and industry experts from India and abroad, ensuring the highest standards of academic integrity and publication ethics. Target Audience: Engineering Faculty and Research Scholars Industry Practitioners and R&D Professionals Postgraduate and Doctoral Students Policy Makers and Innovators in Technology Call for Papers: ParulXplore invites high-quality submissions from researchers and practitioners for upcoming issues. All papers must be original, unpublished, and not under consideration elsewhere.
All articles published in PUXplore: Multidisciplinary Journal of Engineering are licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. Under this license, anyone may read, download, copy, distribute, and share the work for non-commercial purposes, provided that appropriate credit is given to the author(s), the journal, and a link to the license is included. No adaptations, derivatives, or modifications of the work are permitted without prior written permission from the copyright holder. Authors retain copyright and grant the journal the right of first publication under this license.
A Machine Learning Architecture Integrating Spatiotemporal Graph Networks with Differentiable Optimization for Adaptive System Management
This research addresses the fundamental challenge of adaptive system management in complex, dynamic environments by developing a novel integrated machine learning framework that bridges the historical divide between sophisticated spatiotemporal modeling and constraint-aware optimization. Traditional approaches typically treat relational reasoning and decision optimization as sequential processes, fundamentally limiting their ability to capture the intricate interdependencies and evolving constraints that characterize real-world systems. In response, we propose a unified architecture that seamlessly integrates dynamic spatiotemporal graph neural networks with differentiable optimization layers, enabling bidirectional information flow and end-to-end learning of both system dynamics and optimal decisions. The framework incorporates three core innovations: a dynamic graph construction module that learns time-varying relational structures from multimodal data, hierarchical spatiotemporal blocks that capture multi-scale temporal patterns while maintaining spatial context, and differentiable optimization mechanisms that incorporate domain constraints directly into the learning process while supporting regime-aware adaptation. Comprehensive experimental evaluation across two distinct application domains—campus infrastructure management and financial portfolio optimization—demonstrates the framework's superior performance and practical utility. In campus infrastructure forecasting, the framework achieves a 16.3% reduction in mean absolute error compared to the strongest baseline, with particular strength during anomalous events and regime transitions where traditional approaches falter. For portfolio optimization, the framework delivers a Sharpe ratio of 1.38 during the out-of-sample period (2017-2022), representing a 23.2% improvement over contemporary deep learning approaches and a 55.1% enhancement over traditional risk parity strategies, while simultaneously reducing maximum drawdown by 41%. Crucially, the framework exhibits genuine predictive adaptation capabilities, proactively adjusting to changing conditions rather than reacting to realized outcomes, as evidenced during market crises where it began risk reduction weeks before market troughs. Ablation studies confirm that each architectural component contributes significantly to overall performance, with the integrated architecture yielding synergistic improvements unavailable to sequential approaches. The framework demonstrates robust generalization across domains, maintaining 90% of domain-specific performance when transferred between fundamentally different applications with moderate fine-tuning. Computational analysis confirms practical deployment feasibility, with inference latency under 200 milliseconds for typical system sizes, while interpretability mechanisms provide actionable insights for domain experts. This research establishes a new paradigm for adaptive system management, offering both immediate practical value for specific applications and a foundational architecture for broader advances in intelligent decision-making for complex systems characterized by relational dependencies, temporal dynamics, and evolving constraints.
integrated machine learning architecture
Spatiotemporal Graph Neural Networks (ST-GNNs)
Constraint Satisfaction in Neural Networks
Copyright (c) 2026 Dr. Sanjay Agal, Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Neuro-Symbolic AI: Combining Symbolic Reasoning with Neural Networks for Explainable Decision-Making in Interdisciplinary Domains
Artificial Intelligence is performing well in solving some problems but is facing major challenges, as it tries to balance between the models' expressiveness and its interpretation. Despite their expertise in pattern recognition from unstructured data, neural networks remain significantly ambiguous 'black boxes.' Conversely, symbolic reasoning systems provide transparent, rule-based decision-making but struggle with data-driven adaptability. We are illustrating a framework for Neuro-Symbolic AI that describes the integration of neural networks with symbolic reasoning, so as to achieve both the powerful learning and explainable inference. We propose a bidirectional hybrid architecture comprising three layers: (1) a neural processing layer for feature extraction from unstructured data; (2) a symbolic reasoning layer encoding domain knowledge through knowledge graphs and rule-based systems; and (3) an integration layer facilitating semantic communication via differentiable programming. Through experiments on Visual Question Answering (achieving 96.4% accuracy), Natural Language Inference (88.7% accuracy), and Robotics Navigation (94.3% success rate), we demonstrate consistent improvements of 5–20% in accuracy and 2–3× gains in explainability over pure neural or symbolic baselines. This integrated framework have the capacity to deliver interpretable, robust, and generalizable AI in certain critical applications in healthcare, finance, and autonomous systems, The paper addresses scalability challenges, discusses ethical implications, and identifies future research directions including automated knowledge extraction, advanced neural-symbolic interfaces, and real-time adaptive reasoning.
Copyright (c) 2026 Dr. Manoj Kumar Mishra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Smart home automation systems, enhanced through artificial intelligence (AI) and Internet of Things (IoT) technologies, are rapidly transforming traditional living spaces into intelligent, adaptive environments. This study examines the integration of machine learning algorithms, interconnected sensors, and predictive analytics to automate household appliance management, optimize energy consumption, and bolster security. The system utilizes real-time data analysis to adjust operations based on user behavior and ambient conditions, delivering greater energy efficiency, convenience, and safety. Experimental results demonstrate a reduction in manual intervention, significant energy savings, and improved responsiveness in appliance control. The research highlights the effectiveness of AI-powered automation in supporting sustainable, user-friendly homes and outlines future directions for deeper personalization, enhanced security, and scalable design in smart home ecosystems.
Artificial Intelligence of Things (AIoT)
Copyright (c) 2026 PAVAN KUMAR RODDA, Vivek Dave, Krishna Reddy Yaramala, Rishwanth Korlimalla, Vinay Kumar Mule (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Deepfake Detection Using ResNeXt-50 and LSTM Neural Networks
Recent advances in deep learning methods have made it possible to create extremely convincing synthetic media, known by the general public as deepfake videos, that carry enormous dangers such as spreading misinformation, political manipulation, financial scams, and individual blackmail. In this research, we present a system based on deep learning to automate the detection of deepfake videos. In the proposed methodology, we adopt the usage of the ResNeXt-50 architecture to learn the effective frame- level features of the video, which pass through a Long Short-Term Memory (LSTM) network that recognizes the relationships over the whole video. To enhance the generalizability of the model, we adopt a sequential setup with ReLU activation and dropout regularization. To evaluate the model’s effectiveness, we utilize benchmark datasets of deepfakes, and our results show that the model of ResNeXt-50 + LSTM is highly effective in distinguishing between real and manipulated videos. The results of this research provide a significant demonstration of the benefit of fusing multi- dimensional data (i.e., spatial and temporal data) to construct a reliable model to identify deepfakes.
Copyright (c) 2026 PAVAN KUMAR RODDA (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Data Analytics Framework for Measuring the Efficacy of Project Based Learning on SDG Focused FinTech Projects in Indian Universities
This study addresses the critical challenge of evaluating the efficacy of innovative pedagogical approaches in technology education by developing and validating a comprehensive data analytics framework for assessing Project-Based Learning in Sustainable Development Goals-focused FinTech education within Indian universities. The research emerges from the identified gap in empirical measurement tools capable of capturing the multi-dimensional outcomes of experiential learning in complex, interdisciplinary domains. Employing a Design Science Research methodology, the study conceptualizes and implements a novel framework that integrates cognitive, behavioral, and affective dimensions of learning through multi-modal data collection and advanced analytical techniques. The experimental implementation involved a longitudinal case study with 42 participants in a Master's level Sustainable FinTech Solutions course over a 16-week semester. Results demonstrate significant improvements across all dimensions: cognitive assessments revealed substantial knowledge gains with effect sizes of 1.74, behavioral analysis showed progressive enhancement of collaboration patterns and problem-solving capabilities, and affective measures indicated positive transformations in engagement, motivation, and sustainability awareness. Correlation analysis revealed strong interrelationships between dimensions, particularly between behavioral and affective factors (r = 0.73), highlighting their synergistic role in learning processes. The framework successfully provided real-time, actionable insights for pedagogical refinement, enabling early identification of at-risk students and targeted interventions that improved outcomes by 23%. The study makes significant contributions to educational technology by demonstrating how data-driven approaches can transform the assessment of complex competency development, while simultaneously advancing the integration of sustainability principles in FinTech education. The findings offer practical guidelines for educators and institutions seeking to enhance their pedagogical practices through evidence-based approaches that align with both educational objectives and global sustainability imperatives.
Copyright (c) 2025 Dr. Sanjay Agal, Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Fifth-generation (5G) frameworks are now at the leading edge of the worldwide digitization due to the quick development of wireless technology for communication. Automated places, automated factories, driverless cars, distant medical services, and engaging activities like virtual and augmented reality are merely some of the broad purposes made possible by 5G's possibilities, which include ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). Nevertheless, 5G communications unparalleled size, intricacy, and rapid evolution provide serious problems for safety, flexibility, conservation of energy, and productivity improvement. AI improves anomaly identification, invasion mitigation, and continuous surveillance of possible breaches. AI networks provide novel safety vulnerabilities due to their susceptibility to inductive breaches, data compromise, and adversary management. With an emphasis on its use across infrastructure construction, operation efficiency, and privacy, this study offers a thorough examination of the incorporation of AI with 5G networking. It primarily explores the use of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) approaches to enhance traffic identification, simplify system division, enhance the utilization of resources, and facilitate autonomous systems. High Quality of Service (QoS) may be maintained regardless of crowded, adequate-demand settings by employing AI-driven solutions that enable providers to proactively predict and react to changing circumstances affecting the network. It promotes longevity by facilitating adaptive distribution of loads among the network elements and reducing utilization of energy. The abstract also emphasizes AI's hybrid safety function.
Copyright (c) 2026 SONA D SOLANKI, DR. PREM PAL SINGH, DR. KALPESH R JADAV, ASHA D SOLANKI (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Natural Language Processing (NLP) for Multilingual Analysis
This paper explores the application of Natural Language Processing (NLP) techniques for multilingual analysis, addressing the challenges and methodologies involved in processing and analyzing text across diverse languages. It reviews recent advancements in language models, data preprocessing, and system architectures that facilitate effective multilingual understanding. The study proposes a comprehensive framework and evaluates its performance using benchmark datasets, demonstrating improvements in accuracy for multilingual text processing tasks. The results underscore the potential impact of such systems on enhancing global communication, information retrieval, and machine translation services.
Copyright (c) 2026 bhavik tadvi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Depth-Adaptive Routing Mechanisms in Recursive Language Models: A Comprehensive Analysis of Computational Efficiency and Performance Trade-offs
The exponential proliferation of large language models (LLMs) has engendered substantial computational challenges, necessitating innovative methodologies to optimize inference efficiency while preserving performance accuracy. This paper offers a comprehensive analysis of depth-adaptive routing mechanisms in recursive language models, scrutinizing their theoretical foundations, implementation strategies, and comparative performance across a spectrum of architectures. Through a systematic evaluation of Q1-journal studies, we examine how dynamic routing strategies empower models to adaptively allocate computational resources in accordance with input complexity and task exigencies. Our findings elucidate that depth-adaptive routing mechanisms realize an average accuracy enhancement of 17.79% (σ = 13.70) while concurrently diminishing computational overhead by 21.01% (σ = 12.44) across various model architectures. We propose a cohesive mathematical framework for characterizing adaptive routing functions and present empirical evidence illustrating that mixture-of-experts architectures with expert-choice routing surpass traditional token-choice methods, achieving 50% swifter inference speeds with 90% memory efficiency. The theoretical analysis establishes complexity bounds for disparate routing strategies, demonstrating that adaptive token routing attains O(n log n d) time complexity in contrast to O(n²d) for dense transformers. These contributions furnish foundational insights for the development of next-generation efficient language models and establish benchmarks for evaluating adaptive routing mechanisms in production deployments.
Copyright (c) 2025 Nikunj Bhavsar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
In order to determine the type of evaluation with the attributes utilised, the methods employed, the methods most frequently used, and the methods with the greatest performance, this research was carried out using a literature review method to analyse various studies. Based on pre-established criteria, 40 publications were selected from research conducted between 2016 and July 2021 for this study. Four research topics estimation, classification, clustering, and association—were determined by this review. Four research topics fundamentals, technical, sentiment analysis, and even a combination with analyses that make use of their respective qualities and datasets—are the results of this study. Thirty-one different methods have been identified for stock price prediction. The most popular approaches are LSTM, MLP, RF, and SVM. Furthermore, MLP is a technique that yields the best results, with an LSTM of 70% and a performance of 71.63%. For better precision, it is advised to employ deep learning, ensemble approaches, mixed machine learning, and input attribute selection at pre-processing.
Stock Price Prediction, LSTM, Machine Learning, Time Series Forecasting, Financial Analytics, Plotly Dash.
Copyright (c) 2026 Pranjalee Sharma (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Breast Cancer Prediction using Machine Learning Techniques
Breast cancer is one of the leading causes of death among women worldwide. Early detection is crucial for increasing survival rates. In this study, we evaluate and compare several supervised machine learning algorithms for classifying breast cancer tumors as benign or malignant using the Wisconsin Breast Cancer Dataset. The preprocessing stage involved handling missing values, feature scaling, and skewness removal using mathematical transformations. Feature selection was applied to improve classification performance. Six algorithms—Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine—were implemented in Python (Anaconda, Jupyter Notebook) and evaluated using 10-fold cross-validation. Experimental results demonstrate that Logistic Regression and SVM achieved the highest accuracy (98.24%), surpassing results reported in previous studies. This paper highlights the potential of these techniques for developing robust Computer-Aided Diagnosis systems.
Copyright (c) 2026 Sandip Chakraborty, Aritra Das, Subhadeep Das, Arya Manikya Sinha (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Optimizing Passenger Comfort in Rail Transit via Predictive Jerk Estimation Models
Increasing train acceleration and deceleration can improve system performance in a railway network. However, passengers are also more likely to lose their balance and collapse. The purpose of this article is to investigate the effects of longitudinal vehicle accelerations on passenger comfort and safety. In addition to the results of previous empirical studies on the maximum acceleration that train passengers can tolerate, the literature review combines two different academic fields to investigate the physiological and kinesiological impacts of acceleration on balance [2]. This Research paper proposes a novel machine learning (ML)-based approach for real-time jerk prediction to enhance train passenger stability. We look into a variety of machine learning methods, including deep learning techniques (particularly, recurrent neural networks, or LSNs). and supervised learning models (Random Forest, Support Vector Machines, and Gradient Boosting), [3] to forecast train dynamics using sensor information such as velocity, acceleration, and position. Our method forecasts short jerk incidents using time-series analysis, allowing for real-time passenger alert systems or proactive changes to train operations. We employ rigorous cross-validation to verify the performance of the mentioned algorithms, evaluating F1-score, accuracy, precision, and recall. The results were outstanding and demonstrate that models based on deep learning, particularly LSTM networks, outperform traditional methods by offering higher prediction accuracy in dynamic scenarios.
JERK PREDICTION, LONG SHORT-TERM MEMORY (LSTM), RANDOM FOREST (RF), SUPPORT VECTOR MACHINE (SVM),GRADIENT BOOSTING (GB)
Copyright (c) 2025 Dr Syed Ibad Ali (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Convolutional Neural Network BasedCotton Leaf Disease Classification LeveragingTransfer Learning
Cotton is one of the most important crops in Indian agriculture, serving as the primary raw material for the textile industry. However, its productivity is severely impacted by various leaf diseases, making the early and accurate disease detection crucial for effective crop management and yield improvement. Traditional disease identification methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. To address these challenges, this study proposes an automated cotton leaf disease classification system using deep convolutional neural networks (CNNs) with transfer learning. We evaluate three pretrained CNN models—InceptionV3, ResNet152V2, and MobileNetV2—for image-based disease classification. These models leverage transfer learning to enhance feature extraction and classification accuracy while reducing training time. The system is trained and tested on a benchmark cotton leaf disease dataset, and its performance is assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that transfer learning significantly improves classification performance, enabling rapid and reliable disease identification and empowering farmers with an automated tool to take timely preventive measures. This research contributes to smart agriculture by integrating AI-driven solutions for sustainable farming and improved cotton yield. \keywords{Deep Learning, Convolutional Neural Network, Transfer Learning, Cotton Leaf Disease Classification, Smart Agriculture.
Copyright (c) 2025 Saikat Samanta, Subhayu Ghosh, Debtanu Ghosh, Dr. Nanda Dulal Jana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Fraud Detection in Blockchain Transactions Using Graph-Based Deep Learning
A thorough analysis of graph-based deep learning techniques for blockchain transaction fraud detection is presented in this paper. Blockchain networks' decentralized and pseudonymous structure creates special difficulties that make conventional fraud detection strategies useless, calling for sophisticated methods that can examine intricate transactional relationships. In comparison to traditional machine learning techniques, we perform a systematic evaluation of graph neural network (GNN) architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), showing their superior ability to identify complex fraud patterns like money laundering, Ponzi schemes, and phishing attacks. Our work demonstrates how these models take advantage of the intrinsic graph structure of blockchain transactions by using network topology, node features, and edge attributes to accurately identify anomalous activity. The report also looks at important issues facing the industry, such as the need for cross-chain fraud detection capabilities, interpretability requirements for regulatory compliance, and scalability constraints for real-time analysis. We demonstrate through experimental validation on real-world blockchain datasets that attention-based GNNs maintain computational efficiency while achieving notable gains in detection accuracy (F1-score of 0.91). Promising research directions are outlined in the paper's conclusion, including the creation of explainable AI frameworks for forensic investigations and the incorporation of temporal graph networks for dynamic fraud pattern recognition. These developments establish graph-based deep learning as a game-changing strategy for protecting blockchain ecosystems from changing financial crimes.
Copyright (c) 2025 Matangi Gandhi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Detailed Review and Implementation of Dense Neural Networks for Handwritten Digit Recognition
Handwritten digit recognition is an important task in computer vision and deep learning research. It serves as a standard for measuring how well classification algorithms perform. Convolutional neural networks (CNNs) lead this area because they excel at capturing spatial relationships, but fully connected dense neural networks can still perform well if they are properly preprocessed and adjusted. This paper offers a detailed review and practical implementation of a dense feedforward neural network for classifying grayscale handwritten digit images from an MNIST-like dataset. We explain the data preprocessing steps, architecture design, training methods, and evaluation metrics. The model, built using TensorFlow Keras, reaches over 97% validation accuracy in 10 epochs. This shows that dense architectures are still a good choice for simple classification tasks. We also compare our results with previous studies and discuss the model's strengths, weaknesses, and possible improvements.
Copyright (c) 2025 shruti patel (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Cybersecurity Challenges in Industrial Control Systems: A Multidisciplinary Review
<span lang="EN-US" style="font-size: 11.0pt; mso-bidi-font-size: 9.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Generally speaking, this study explores the specific cybersecurity problems faced by Industrial Control Systems (ICS). It emphasizes that these problems are multifaceted due to the integration of various technologies and operational frameworks. The research looks into how ICS are critically vulnerable to cyber threats, collecting and then analyzing data on current vulnerabilities, common threat vectors, and also notable incident case studies across different industrial sectors. Key findings generally reveal that the merging of operational tech with info tech actually intensifies security complexities, and this leads to increased vulnerability to cyber-attacks. Within healthcare, the above findings underscore the heightened risks connected with relying on ICS for essential infrastructure, including critical medical devices, and also hospital operational systems, which highlights the need for robust cybersecurity measures. The implications extend beyond the direct ICS scope, suggesting that enhanced cybersecurity strategies, in most cases, can significantly improve healthcare systems' resilience against cyber threats. By fostering interdisciplinary collaboration between cybersecurity experts, engineers, and healthcare professionals, this research lays the foundation for developing comprehensive strategies that can protect critical industrial environments, and thus ultimately contribute to a more secure and efficient healthcare infrastructure
Copyright (c) 2025 Ms. Niyati Dhirubhai Odedra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Technologies Redefining the Future: A Glimpse into the Age of Intelligence
The 21st century is experiencing a transformative wave driven by smart technologies that are reshaping every aspect of human life, from urban infrastructure to personal healthcare. This paper examines how innovations in artificial intelligence (AI), the Internet of Things (IoT), 5G connectivity, automation, and robotics are revolutionising various sectors, including smart cities, healthcare, industry, and everyday living. Smart technologies facilitate efficient urban planning, real-time health monitoring, intelligent automation in industries, and personalised consumer experiences. However, this transformation also presents challenges related to data privacy, technological dependency, and shifts in the employment landscape. The study highlights the importance of ethical integration and inclusive digital growth, concluding that responsible innovation is crucial for sustainable progress in this digital era.
Smart Technologies, Artificial Intelligence (AI), Internet of Things (IoT), Smart Cities, Smart Healthcare, Automation, Robotics, 5G Connectivity, Digital Transformation, Ethical Challenges
Copyright (c) 2025 Dr Bhavna Bajpai Bhavna (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Agriculture plays a crucial role in human lives, with around 70% of the population directly or indirectly involved in it. However, traditional approaches lack tools to identify diseases in crops and increase agricultural output. Early detection of crop diseases is vital as they impact plant growth and pose a significant threat to food security. Machine Learning (ML) models have been used to identify and categorize agricultural diseases, but recent advancements in Deep Learning show promise for improved accuracy. This proposed method utilizes convolutional neural networks and deep neural networks to effectively and accurately identify and recognize crop disease symptoms. By training deep learning models using large publicly available datasets, it becomes possible to detect plant diseases on a large scale.
Plant Disease Detection, ResNet50, Machine learning, Deep Neural Network, Transfer Learning
Copyright (c) 2026 Mohammad Arif, Arun Kumar Marandi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Comparative Study and Design for Construction of An Automatic Sanitizing Floor Cleaning Robot
This research paper proposes the development and deployment of an automated sanitizing robot integrated with a camera. The paper also compares this robot for cleaning floors to a robot for cleaning floors. G power software is used to find the sample size for each group, taking into account predefined parameters and aiming for the best performance with a maximum error of 0.05. This is done to make sure the results are correct. A fixed sample size of 20 is selected for each group for analytical purposes. The design of the automatic controlled sanitizing robot incorporates various components such as Arduino, motor shield, camera, and chassis. The Arduino IDE app is employed to program the Arduino Board. The effectiveness and reliability of the proposed methods are evaluated using a 2-tailed statistical analysis. Additionally, this paper suggests that the sanitizing robot outperforms the floor cleaning robot when it comes to spraying on a flat surface.
Copyright (c) 2025 ARUN KUMAR MARANDI ARUN (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Participation of AI Technologies in Financial Services DEFENSE AGAINST CYBER-ATTACKS
This abstract highlights the role of AI technologies in bolstering the defense against cyber-attacks in financial services. AI empowers organizations to proactively detect and respond to threats by analyzing vast amounts of data. It enables real-time anomaly detection, incident response automation, and fraud prevention. The integration of AI technologies strengthens the security posture of financial institutions and helps mitigate the risks associated with cyber-attacks.
Copyright (c) 2025 Dr. Neelesh Kumar Jain https://creativecommons.org/licenses/by-nc-sa/4.0